{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Z-SCORE归一化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a68c81fcc0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "#arange方法产生2维并且每个维度100个数据\n",
    "data1 = torch.randint(10,100,(2,100)).float()\n",
    "#进行Z-SCORE归一化处理\n",
    "data2 = (data1 - data1.mean()) / data1.std()\n",
    "#对标准化后的数据进行可视化处理\n",
    "plt.plot(data1[0].numpy(),data1[1].numpy(),\"y*\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a68967df98>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data2[0].numpy(),data2[1].numpy(),\"r.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Min-Max标准化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a68c883e10>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Min-Max归一化处理\n",
    "data3 = (data1 - data1.min()) /(data1.max()-data1.min())\n",
    "plt.plot(data3[0].numpy(),data3[1].numpy(),\"g.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征的正则化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.0000,  0.5000, -1.0000],\n",
       "        [ 2.0000,  1.0000,  1.0000],\n",
       "        [ 4.0000, 10.0000,  2.0000]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_data = [\n",
    "    [1.0, 0.5, -1.0],\n",
    "    [2.0, 1.0, 1.0],\n",
    "    [4.0, 10.0, 2.0]\n",
    "]\n",
    "data4 = torch.Tensor(tmp_data)\n",
    "data4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 求p-norm范数（ｐ指定为３，可自行指定ｐ值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.6667,  0.3333, -0.6667],\n",
       "        [ 0.8165,  0.4082,  0.4082],\n",
       "        [ 0.3651,  0.9129,  0.1826]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4/(data4.pow(2).sum(dim=1).pow(1/2).unsqueeze(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.5000],\n",
       "        [ 2.4495],\n",
       "        [10.9545]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4.pow(2).sum(dim=1).pow(1/2).unsqueeze(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
